[WIP] Adapt querying logic

This change the logic to query the tree for nearest neighbors.
Start with a simple sequential query for each point.

tests/test_conf.py

@@ -14,20 +14,29 @@ def test_creation_deletion(n, d, leaf_size):
     tree = kdtree.KDTree(X, leaf_size=256)
     del tree
 
-@pytest.mark.skip(reason="The query is being refactored.")
 @pytest.mark.parametrize("n", [10, 100, 1000, 10000])
 @pytest.mark.parametrize("d", [10, 100])
 @pytest.mark.parametrize("k", [1, 2, 5, 10])
 @pytest.mark.parametrize("leaf_size", [256, 1024])
-def test_against_sklearn(n, d, k, leaf_size):
-    np.random.seed(1)
+def test_against_sklearn(n, d, k, leaf_size, n_query=1):
+    np.random.seed(2)
     X = np.random.rand(n, d)
-    query_points = np.random.rand(n, d)
+    query_points = np.random.rand(n_query, d)
 
     tree = kdtree.KDTree(X, leaf_size=256)
     skl_tree = KDTree(X, leaf_size=256)
 
-    closests = np.zeros((n, k), dtype=np.int32)
-    tree.query(query_points, closests)
-    skl_closests = skl_tree.query(query_points, k=k, return_distance=False).astype(np.int32)
-    np.testing.assert_equal(closests, skl_closests)
+    knn_indices = np.zeros((n_query, k), dtype=np.int32)
+    knn_distances = np.zeros((n_query, k), dtype=np.float64)
+    tree.query(query_points, knn_indices, knn_distances)
+    skl_knn_distances, skl_knn_indices = skl_tree.query(
+        query_points,
+        k=k,
+        return_distance=True
+    )
+
+    # Adapting types
+    skl_knn_indices = skl_knn_indices.astype(np.int32)
+
+    np.testing.assert_equal(knn_indices, skl_knn_indices)
+    np.testing.assert_almost_equal(knn_distances, skl_knn_distances)